Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations209306
Missing cells0
Missing cells (%)0.0%
Duplicate rows31
Duplicate rows (%)< 0.1%
Total size in memory39.9 MiB
Average record size in memory200.0 B

Variable types

DateTime1
Categorical14
Boolean1
Numeric9

Alerts

Dataset has 31 (< 0.1%) duplicate rowsDuplicates
crash_type is highly overall correlated with injury_severity and 1 other fieldsHigh correlation
injuries_fatal is highly overall correlated with injury_severity and 1 other fieldsHigh correlation
injuries_non_incapacitating is highly overall correlated with injuries_totalHigh correlation
injuries_reported_not_evident is highly overall correlated with injuries_totalHigh correlation
injuries_total is highly overall correlated with injuries_non_incapacitating and 1 other fieldsHigh correlation
injury_severity is highly overall correlated with crash_type and 2 other fieldsHigh correlation
most_severe_injury is highly overall correlated with crash_type and 2 other fieldsHigh correlation
roadway_surface_cond is highly overall correlated with weather_conditionHigh correlation
weather_condition is highly overall correlated with roadway_surface_condHigh correlation
traffic_control_device is highly imbalanced (63.3%)Imbalance
weather_condition is highly imbalanced (66.9%)Imbalance
alignment is highly imbalanced (92.7%)Imbalance
roadway_surface_cond is highly imbalanced (57.4%)Imbalance
road_defect is highly imbalanced (72.0%)Imbalance
intersection_related_i is highly imbalanced (72.3%)Imbalance
injuries_fatal is highly imbalanced (99.1%)Imbalance
injuries_total has 154789 (74.0%) zerosZeros
injuries_incapacitating has 202672 (96.8%) zerosZeros
injuries_non_incapacitating has 176306 (84.2%) zerosZeros
injuries_reported_not_evident has 190619 (91.1%) zerosZeros
injuries_no_indication has 6229 (3.0%) zerosZeros
crash_hour has 4487 (2.1%) zerosZeros

Reproduction

Analysis started2025-10-08 23:09:33.283546
Analysis finished2025-10-08 23:11:18.373758
Duration1 minute and 45.09 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct189087
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
Minimum2013-03-03 16:48:00
Maximum2025-01-18 00:17:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-08T23:11:18.481395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:18.623380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

traffic_control_device
Categorical

Imbalance 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
TRAFFIC SIGNAL
123944 
STOP SIGN/FLASHER
49139 
NO CONTROLS
29508 
UNKNOWN
 
4455
OTHER
 
670
Other values (14)
 
1590

Length

Max length24
Median length14
Mean length14.109404
Min length5

Characters and Unicode

Total characters2953183
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRAFFIC SIGNAL
2nd rowTRAFFIC SIGNAL
3rd rowTRAFFIC SIGNAL
4th rowTRAFFIC SIGNAL
5th rowTRAFFIC SIGNAL

Common Values

ValueCountFrequency (%)
TRAFFIC SIGNAL123944
59.2%
STOP SIGN/FLASHER49139
 
23.5%
NO CONTROLS29508
 
14.1%
UNKNOWN4455
 
2.1%
OTHER670
 
0.3%
YIELD468
 
0.2%
PEDESTRIAN CROSSING SIGN247
 
0.1%
OTHER REG. SIGN181
 
0.1%
LANE USE MARKING153
 
0.1%
FLASHING CONTROL SIGNAL150
 
0.1%
Other values (9)391
 
0.2%

Length

2025-10-08T23:11:18.764961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
signal124094
30.0%
traffic123944
29.9%
stop49139
 
11.9%
sign/flasher49139
 
11.9%
no29520
 
7.1%
controls29508
 
7.1%
unknown4455
 
1.1%
other969
 
0.2%
sign552
 
0.1%
yield468
 
0.1%
Other values (18)2066
 
0.5%

Most occurring characters

ValueCountFrequency (%)
S302949
10.3%
I299464
10.1%
A298492
10.1%
F297281
10.1%
N247764
8.4%
R205018
 
6.9%
204548
 
6.9%
T204052
 
6.9%
L204032
 
6.9%
G174935
 
5.9%
Other values (15)514648
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)2953183
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S302949
10.3%
I299464
10.1%
A298492
10.1%
F297281
10.1%
N247764
8.4%
R205018
 
6.9%
204548
 
6.9%
T204052
 
6.9%
L204032
 
6.9%
G174935
 
5.9%
Other values (15)514648
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2953183
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S302949
10.3%
I299464
10.1%
A298492
10.1%
F297281
10.1%
N247764
8.4%
R205018
 
6.9%
204548
 
6.9%
T204052
 
6.9%
L204032
 
6.9%
G174935
 
5.9%
Other values (15)514648
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2953183
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S302949
10.3%
I299464
10.1%
A298492
10.1%
F297281
10.1%
N247764
8.4%
R205018
 
6.9%
204548
 
6.9%
T204052
 
6.9%
L204032
 
6.9%
G174935
 
5.9%
Other values (15)514648
17.4%

weather_condition
Categorical

High correlation  Imbalance 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
CLEAR
164700 
RAIN
21703 
CLOUDY/OVERCAST
 
7533
SNOW
 
6871
UNKNOWN
 
6534
Other values (7)
 
1965

Length

Max length24
Median length5
Mean length5.3545813
Min length4

Characters and Unicode

Total characters1120746
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCLEAR
2nd rowCLEAR
3rd rowCLEAR
4th rowCLEAR
5th rowCLEAR

Common Values

ValueCountFrequency (%)
CLEAR164700
78.7%
RAIN21703
 
10.4%
CLOUDY/OVERCAST7533
 
3.6%
SNOW6871
 
3.3%
UNKNOWN6534
 
3.1%
OTHER627
 
0.3%
FREEZING RAIN/DRIZZLE510
 
0.2%
FOG/SMOKE/HAZE360
 
0.2%
SLEET/HAIL308
 
0.1%
BLOWING SNOW127
 
0.1%
Other values (2)33
 
< 0.1%

Length

2025-10-08T23:11:18.882278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
clear164700
78.4%
rain21703
 
10.3%
cloudy/overcast7533
 
3.6%
snow6998
 
3.3%
unknown6534
 
3.1%
other627
 
0.3%
freezing510
 
0.2%
rain/drizzle510
 
0.2%
fog/smoke/haze360
 
0.2%
sleet/hail308
 
0.1%
Other values (8)259
 
0.1%

Most occurring characters

ValueCountFrequency (%)
R196158
17.5%
A195147
17.4%
C179798
16.0%
E175854
15.7%
L173488
15.5%
N49484
 
4.4%
O30106
 
2.7%
I23703
 
2.1%
S15297
 
1.4%
U14067
 
1.3%
Other values (15)67644
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1120746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R196158
17.5%
A195147
17.4%
C179798
16.0%
E175854
15.7%
L173488
15.5%
N49484
 
4.4%
O30106
 
2.7%
I23703
 
2.1%
S15297
 
1.4%
U14067
 
1.3%
Other values (15)67644
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1120746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R196158
17.5%
A195147
17.4%
C179798
16.0%
E175854
15.7%
L173488
15.5%
N49484
 
4.4%
O30106
 
2.7%
I23703
 
2.1%
S15297
 
1.4%
U14067
 
1.3%
Other values (15)67644
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1120746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R196158
17.5%
A195147
17.4%
C179798
16.0%
E175854
15.7%
L173488
15.5%
N49484
 
4.4%
O30106
 
2.7%
I23703
 
2.1%
S15297
 
1.4%
U14067
 
1.3%
Other values (15)67644
 
6.0%

lighting_condition
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
DAYLIGHT
134109 
DARKNESS, LIGHTED ROAD
53378 
DARKNESS
 
7436
DUSK
 
6323
UNKNOWN
 
4336

Length

Max length22
Median length8
Mean length11.35761
Min length4

Characters and Unicode

Total characters2377216
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDAYLIGHT
2nd rowDARKNESS, LIGHTED ROAD
3rd rowDAYLIGHT
4th rowDAYLIGHT
5th rowDAYLIGHT

Common Values

ValueCountFrequency (%)
DAYLIGHT134109
64.1%
DARKNESS, LIGHTED ROAD53378
 
25.5%
DARKNESS7436
 
3.6%
DUSK6323
 
3.0%
UNKNOWN4336
 
2.1%
DAWN3724
 
1.8%

Length

2025-10-08T23:11:19.055529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T23:11:19.186600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
daylight134109
42.4%
darkness60814
19.2%
lighted53378
 
16.9%
road53378
 
16.9%
dusk6323
 
2.0%
unknown4336
 
1.4%
dawn3724
 
1.2%

Most occurring characters

ValueCountFrequency (%)
D311726
13.1%
A252025
10.6%
L187487
 
7.9%
I187487
 
7.9%
G187487
 
7.9%
H187487
 
7.9%
T187487
 
7.9%
Y134109
 
5.6%
S127951
 
5.4%
R114192
 
4.8%
Other values (8)499778
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)2377216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D311726
13.1%
A252025
10.6%
L187487
 
7.9%
I187487
 
7.9%
G187487
 
7.9%
H187487
 
7.9%
T187487
 
7.9%
Y134109
 
5.6%
S127951
 
5.4%
R114192
 
4.8%
Other values (8)499778
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2377216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D311726
13.1%
A252025
10.6%
L187487
 
7.9%
I187487
 
7.9%
G187487
 
7.9%
H187487
 
7.9%
T187487
 
7.9%
Y134109
 
5.6%
S127951
 
5.4%
R114192
 
4.8%
Other values (8)499778
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2377216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D311726
13.1%
A252025
10.6%
L187487
 
7.9%
I187487
 
7.9%
G187487
 
7.9%
H187487
 
7.9%
T187487
 
7.9%
Y134109
 
5.6%
S127951
 
5.4%
R114192
 
4.8%
Other values (8)499778
21.0%

first_crash_type
Categorical

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
TURNING
64157 
ANGLE
52250 
REAR END
42018 
SIDESWIPE SAME DIRECTION
20116 
PEDESTRIAN
8996 
Other values (13)
21769 

Length

Max length28
Median length24
Mean length9.2783962
Min length5

Characters and Unicode

Total characters1942024
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTURNING
2nd rowTURNING
3rd rowREAR END
4th rowANGLE
5th rowREAR END

Common Values

ValueCountFrequency (%)
TURNING64157
30.7%
ANGLE52250
25.0%
REAR END42018
20.1%
SIDESWIPE SAME DIRECTION20116
 
9.6%
PEDESTRIAN8996
 
4.3%
PEDALCYCLIST5337
 
2.5%
PARKED MOTOR VEHICLE4893
 
2.3%
FIXED OBJECT4742
 
2.3%
SIDESWIPE OPPOSITE DIRECTION1839
 
0.9%
HEAD ON1790
 
0.9%
Other values (8)3168
 
1.5%

Length

2025-10-08T23:11:19.359236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
turning64157
20.3%
angle52250
16.5%
rear44046
13.9%
end42018
13.3%
sideswipe21955
 
6.9%
direction21955
 
6.9%
same20116
 
6.4%
pedestrian8996
 
2.8%
object5501
 
1.7%
pedalcyclist5337
 
1.7%
Other values (15)30187
9.5%

Most occurring characters

ValueCountFrequency (%)
E278148
14.3%
N257408
13.3%
R195351
10.1%
I179140
9.2%
A137590
 
7.1%
T116926
 
6.0%
G116407
 
6.0%
D112555
 
5.8%
107212
 
5.5%
S81220
 
4.2%
Other values (15)360067
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1942024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E278148
14.3%
N257408
13.3%
R195351
10.1%
I179140
9.2%
A137590
 
7.1%
T116926
 
6.0%
G116407
 
6.0%
D112555
 
5.8%
107212
 
5.5%
S81220
 
4.2%
Other values (15)360067
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1942024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E278148
14.3%
N257408
13.3%
R195351
10.1%
I179140
9.2%
A137590
 
7.1%
T116926
 
6.0%
G116407
 
6.0%
D112555
 
5.8%
107212
 
5.5%
S81220
 
4.2%
Other values (15)360067
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1942024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E278148
14.3%
N257408
13.3%
R195351
10.1%
I179140
9.2%
A137590
 
7.1%
T116926
 
6.0%
G116407
 
6.0%
D112555
 
5.8%
107212
 
5.5%
S81220
 
4.2%
Other values (15)360067
18.5%

trafficway_type
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
NOT DIVIDED
77753 
FOUR WAY
49057 
DIVIDED - W/MEDIAN (NOT RAISED)
34221 
ONE-WAY
12341 
DIVIDED - W/MEDIAN BARRIER
10720 
Other values (15)
25214 

Length

Max length31
Median length26
Mean length14.301353
Min length4

Characters and Unicode

Total characters2993359
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNOT DIVIDED
2nd rowFOUR WAY
3rd rowT-INTERSECTION
4th rowFOUR WAY
5th rowT-INTERSECTION

Common Values

ValueCountFrequency (%)
NOT DIVIDED77753
37.1%
FOUR WAY49057
23.4%
DIVIDED - W/MEDIAN (NOT RAISED)34221
16.3%
ONE-WAY12341
 
5.9%
DIVIDED - W/MEDIAN BARRIER10720
 
5.1%
T-INTERSECTION9233
 
4.4%
OTHER4757
 
2.3%
CENTER TURN LANE2862
 
1.4%
UNKNOWN INTERSECTION TYPE1885
 
0.9%
FIVE POINT, OR MORE1119
 
0.5%
Other values (10)5358
 
2.6%

Length

2025-10-08T23:11:19.527552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
divided122694
23.6%
not112555
21.7%
four49057
 
9.4%
way49057
 
9.4%
44941
 
8.6%
w/median44941
 
8.6%
raised34221
 
6.6%
one-way12341
 
2.4%
barrier10720
 
2.1%
t-intersection9233
 
1.8%
Other values (22)30056
 
5.8%

Most occurring characters

ValueCountFrequency (%)
D448117
15.0%
I363281
12.1%
310510
10.4%
E269611
9.0%
N213380
 
7.1%
O199318
 
6.7%
T162409
 
5.4%
A156774
 
5.2%
R144189
 
4.8%
V123956
 
4.1%
Other values (18)601814
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)2993359
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D448117
15.0%
I363281
12.1%
310510
10.4%
E269611
9.0%
N213380
 
7.1%
O199318
 
6.7%
T162409
 
5.4%
A156774
 
5.2%
R144189
 
4.8%
V123956
 
4.1%
Other values (18)601814
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2993359
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D448117
15.0%
I363281
12.1%
310510
10.4%
E269611
9.0%
N213380
 
7.1%
O199318
 
6.7%
T162409
 
5.4%
A156774
 
5.2%
R144189
 
4.8%
V123956
 
4.1%
Other values (18)601814
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2993359
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D448117
15.0%
I363281
12.1%
310510
10.4%
E269611
9.0%
N213380
 
7.1%
O199318
 
6.7%
T162409
 
5.4%
A156774
 
5.2%
R144189
 
4.8%
V123956
 
4.1%
Other values (18)601814
20.1%

alignment
Categorical

Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
STRAIGHT AND LEVEL
204590 
STRAIGHT ON GRADE
 
2992
CURVE, LEVEL
 
1014
STRAIGHT ON HILLCREST
 
478
CURVE ON GRADE
 
179

Length

Max length21
Median length18
Mean length17.960068
Min length12

Characters and Unicode

Total characters3759150
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSTRAIGHT AND LEVEL
2nd rowSTRAIGHT AND LEVEL
3rd rowSTRAIGHT AND LEVEL
4th rowSTRAIGHT AND LEVEL
5th rowSTRAIGHT AND LEVEL

Common Values

ValueCountFrequency (%)
STRAIGHT AND LEVEL204590
97.7%
STRAIGHT ON GRADE2992
 
1.4%
CURVE, LEVEL1014
 
0.5%
STRAIGHT ON HILLCREST478
 
0.2%
CURVE ON GRADE179
 
0.1%
CURVE ON HILLCREST53
 
< 0.1%

Length

2025-10-08T23:11:19.661824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T23:11:19.772465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
straight208060
33.2%
level205604
32.8%
and204590
32.6%
on3702
 
0.6%
grade3171
 
0.5%
curve1246
 
0.2%
hillcrest531
 
0.1%

Most occurring characters

ValueCountFrequency (%)
417598
11.1%
T416651
11.1%
E416156
11.1%
A415821
11.1%
L412270
11.0%
R213008
 
5.7%
G211231
 
5.6%
S208591
 
5.5%
I208591
 
5.5%
H208591
 
5.5%
Other values (7)630642
16.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)3759150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
417598
11.1%
T416651
11.1%
E416156
11.1%
A415821
11.1%
L412270
11.0%
R213008
 
5.7%
G211231
 
5.6%
S208591
 
5.5%
I208591
 
5.5%
H208591
 
5.5%
Other values (7)630642
16.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3759150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
417598
11.1%
T416651
11.1%
E416156
11.1%
A415821
11.1%
L412270
11.0%
R213008
 
5.7%
G211231
 
5.6%
S208591
 
5.5%
I208591
 
5.5%
H208591
 
5.5%
Other values (7)630642
16.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3759150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
417598
11.1%
T416651
11.1%
E416156
11.1%
A415821
11.1%
L412270
11.0%
R213008
 
5.7%
G211231
 
5.6%
S208591
 
5.5%
I208591
 
5.5%
H208591
 
5.5%
Other values (7)630642
16.8%

roadway_surface_cond
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
DRY
155905 
WET
32908 
UNKNOWN
 
12509
SNOW OR SLUSH
 
6203
ICE
 
1303
Other values (2)
 
478

Length

Max length15
Median length3
Mean length3.5418956
Min length3

Characters and Unicode

Total characters741340
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNKNOWN
2nd rowDRY
3rd rowDRY
4th rowDRY
5th rowUNKNOWN

Common Values

ValueCountFrequency (%)
DRY155905
74.5%
WET32908
 
15.7%
UNKNOWN12509
 
6.0%
SNOW OR SLUSH6203
 
3.0%
ICE1303
 
0.6%
OTHER438
 
0.2%
SAND, MUD, DIRT40
 
< 0.1%

Length

2025-10-08T23:11:19.927286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T23:11:20.057637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
dry155905
70.3%
wet32908
 
14.8%
unknown12509
 
5.6%
snow6203
 
2.8%
or6203
 
2.8%
slush6203
 
2.8%
ice1303
 
0.6%
other438
 
0.2%
sand40
 
< 0.1%
mud40
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
R162586
21.9%
D156025
21.0%
Y155905
21.0%
W51620
 
7.0%
N43770
 
5.9%
E34649
 
4.7%
T33386
 
4.5%
O25353
 
3.4%
U18752
 
2.5%
S18649
 
2.5%
Other values (9)40645
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)741340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R162586
21.9%
D156025
21.0%
Y155905
21.0%
W51620
 
7.0%
N43770
 
5.9%
E34649
 
4.7%
T33386
 
4.5%
O25353
 
3.4%
U18752
 
2.5%
S18649
 
2.5%
Other values (9)40645
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)741340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R162586
21.9%
D156025
21.0%
Y155905
21.0%
W51620
 
7.0%
N43770
 
5.9%
E34649
 
4.7%
T33386
 
4.5%
O25353
 
3.4%
U18752
 
2.5%
S18649
 
2.5%
Other values (9)40645
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)741340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R162586
21.9%
D156025
21.0%
Y155905
21.0%
W51620
 
7.0%
N43770
 
5.9%
E34649
 
4.7%
T33386
 
4.5%
O25353
 
3.4%
U18752
 
2.5%
S18649
 
2.5%
Other values (9)40645
 
5.5%

road_defect
Categorical

Imbalance 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
NO DEFECTS
171730 
UNKNOWN
34426 
WORN SURFACE
 
1000
OTHER
 
912
RUT, HOLES
 
741
Other values (2)
 
497

Length

Max length17
Median length10
Mean length9.5075392
Min length5

Characters and Unicode

Total characters1989985
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNKNOWN
2nd rowNO DEFECTS
3rd rowNO DEFECTS
4th rowNO DEFECTS
5th rowUNKNOWN

Common Values

ValueCountFrequency (%)
NO DEFECTS171730
82.0%
UNKNOWN34426
 
16.4%
WORN SURFACE1000
 
0.5%
OTHER912
 
0.4%
RUT, HOLES741
 
0.4%
SHOULDER DEFECT358
 
0.2%
DEBRIS ON ROADWAY139
 
0.1%

Length

2025-10-08T23:11:20.226096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T23:11:20.358338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no171730
44.8%
defects171730
44.8%
unknown34426
 
9.0%
worn1000
 
0.3%
surface1000
 
0.3%
other912
 
0.2%
rut741
 
0.2%
holes741
 
0.2%
shoulder358
 
0.1%
defect358
 
0.1%
Other values (3)417
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E347326
17.5%
N276147
13.9%
O209445
10.5%
174107
8.7%
S173968
8.7%
T173741
8.7%
C173088
8.7%
F173088
8.7%
D172724
8.7%
U36525
 
1.8%
Other values (10)79826
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1989985
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E347326
17.5%
N276147
13.9%
O209445
10.5%
174107
8.7%
S173968
8.7%
T173741
8.7%
C173088
8.7%
F173088
8.7%
D172724
8.7%
U36525
 
1.8%
Other values (10)79826
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1989985
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E347326
17.5%
N276147
13.9%
O209445
10.5%
174107
8.7%
S173968
8.7%
T173741
8.7%
C173088
8.7%
F173088
8.7%
D172724
8.7%
U36525
 
1.8%
Other values (10)79826
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1989985
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E347326
17.5%
N276147
13.9%
O209445
10.5%
174107
8.7%
S173968
8.7%
T173741
8.7%
C173088
8.7%
F173088
8.7%
D172724
8.7%
U36525
 
1.8%
Other values (10)79826
 
4.0%

crash_type
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
NO INJURY / DRIVE AWAY
117376 
INJURY AND / OR TOW DUE TO CRASH
91930 

Length

Max length32
Median length22
Mean length26.392134
Min length22

Characters and Unicode

Total characters5524032
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO INJURY / DRIVE AWAY
2nd rowNO INJURY / DRIVE AWAY
3rd rowNO INJURY / DRIVE AWAY
4th rowINJURY AND / OR TOW DUE TO CRASH
5th rowNO INJURY / DRIVE AWAY

Common Values

ValueCountFrequency (%)
NO INJURY / DRIVE AWAY117376
56.1%
INJURY AND / OR TOW DUE TO CRASH91930
43.9%

Length

2025-10-08T23:11:20.529489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T23:11:20.627698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
injury209306
15.8%
209306
15.8%
no117376
8.9%
drive117376
8.9%
away117376
8.9%
and91930
7.0%
or91930
7.0%
tow91930
7.0%
due91930
7.0%
to91930
7.0%

Most occurring characters

ValueCountFrequency (%)
1113014
20.1%
R510542
9.2%
A418612
 
7.6%
N418612
 
7.6%
O393166
 
7.1%
Y326682
 
5.9%
I326682
 
5.9%
D301236
 
5.5%
U301236
 
5.5%
J209306
 
3.8%
Other values (8)1204944
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)5524032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1113014
20.1%
R510542
9.2%
A418612
 
7.6%
N418612
 
7.6%
O393166
 
7.1%
Y326682
 
5.9%
I326682
 
5.9%
D301236
 
5.5%
U301236
 
5.5%
J209306
 
3.8%
Other values (8)1204944
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5524032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1113014
20.1%
R510542
9.2%
A418612
 
7.6%
N418612
 
7.6%
O393166
 
7.1%
Y326682
 
5.9%
I326682
 
5.9%
D301236
 
5.5%
U301236
 
5.5%
J209306
 
3.8%
Other values (8)1204944
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5524032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1113014
20.1%
R510542
9.2%
A418612
 
7.6%
N418612
 
7.6%
O393166
 
7.1%
Y326682
 
5.9%
I326682
 
5.9%
D301236
 
5.5%
U301236
 
5.5%
J209306
 
3.8%
Other values (8)1204944
21.8%

intersection_related_i
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size204.5 KiB
True
199324 
False
 
9982
ValueCountFrequency (%)
True199324
95.2%
False9982
 
4.8%
2025-10-08T23:11:20.709483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

damage
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
OVER $1,500
147313 
$501 - $1,500
41210 
$500 OR LESS
20783 

Length

Max length13
Median length11
Mean length11.493072
Min length11

Characters and Unicode

Total characters2405569
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$501 - $1,500
2nd rowOVER $1,500
3rd row$501 - $1,500
4th rowOVER $1,500
5th row$501 - $1,500

Common Values

ValueCountFrequency (%)
OVER $1,500147313
70.4%
$501 - $1,50041210
 
19.7%
$500 OR LESS20783
 
9.9%

Length

2025-10-08T23:11:20.837854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T23:11:20.965702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1,500188523
39.2%
over147313
30.7%
50141210
 
8.6%
41210
 
8.6%
50020783
 
4.3%
or20783
 
4.3%
less20783
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0459822
19.1%
271299
11.3%
$250516
10.4%
5250516
10.4%
1229733
9.6%
,188523
7.8%
R168096
 
7.0%
E168096
 
7.0%
O168096
 
7.0%
V147313
 
6.1%
Other values (3)103559
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)2405569
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0459822
19.1%
271299
11.3%
$250516
10.4%
5250516
10.4%
1229733
9.6%
,188523
7.8%
R168096
 
7.0%
E168096
 
7.0%
O168096
 
7.0%
V147313
 
6.1%
Other values (3)103559
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2405569
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0459822
19.1%
271299
11.3%
$250516
10.4%
5250516
10.4%
1229733
9.6%
,188523
7.8%
R168096
 
7.0%
E168096
 
7.0%
O168096
 
7.0%
V147313
 
6.1%
Other values (3)103559
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2405569
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0459822
19.1%
271299
11.3%
$250516
10.4%
5250516
10.4%
1229733
9.6%
,188523
7.8%
R168096
 
7.0%
E168096
 
7.0%
O168096
 
7.0%
V147313
 
6.1%
Other values (3)103559
 
4.3%
Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
UNABLE TO DETERMINE
58316 
FAILING TO YIELD RIGHT-OF-WAY
42914 
FOLLOWING TOO CLOSELY
19084 
DISREGARDING TRAFFIC SIGNALS
14591 
IMPROPER TURNING/NO SIGNAL
12643 
Other values (35)
61758 

Length

Max length80
Median length75
Mean length25.296924
Min length6

Characters and Unicode

Total characters5294798
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNABLE TO DETERMINE
2nd rowIMPROPER TURNING/NO SIGNAL
3rd rowFOLLOWING TOO CLOSELY
4th rowUNABLE TO DETERMINE
5th rowDRIVING SKILLS/KNOWLEDGE/EXPERIENCE

Common Values

ValueCountFrequency (%)
UNABLE TO DETERMINE58316
27.9%
FAILING TO YIELD RIGHT-OF-WAY42914
20.5%
FOLLOWING TOO CLOSELY19084
 
9.1%
DISREGARDING TRAFFIC SIGNALS14591
 
7.0%
IMPROPER TURNING/NO SIGNAL12643
 
6.0%
FAILING TO REDUCE SPEED TO AVOID CRASH10676
 
5.1%
IMPROPER OVERTAKING/PASSING8302
 
4.0%
DISREGARDING STOP SIGN6749
 
3.2%
IMPROPER LANE USAGE6462
 
3.1%
NOT APPLICABLE5241
 
2.5%
Other values (30)24328
11.6%

Length

2025-10-08T23:11:21.162951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
to122945
16.9%
unable58316
 
8.0%
determine58316
 
8.0%
failing53590
 
7.4%
yield43046
 
5.9%
right-of-way42914
 
5.9%
improper29747
 
4.1%
disregarding22907
 
3.2%
too19084
 
2.6%
closely19084
 
2.6%
Other values (106)256744
35.3%

Most occurring characters

ValueCountFrequency (%)
E518312
 
9.8%
517387
 
9.8%
I516252
 
9.8%
N372413
 
7.0%
O369603
 
7.0%
T321967
 
6.1%
R315619
 
6.0%
L309783
 
5.9%
A307956
 
5.8%
G265384
 
5.0%
Other values (23)1480122
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5294798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E518312
 
9.8%
517387
 
9.8%
I516252
 
9.8%
N372413
 
7.0%
O369603
 
7.0%
T321967
 
6.1%
R315619
 
6.0%
L309783
 
5.9%
A307956
 
5.8%
G265384
 
5.0%
Other values (23)1480122
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5294798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E518312
 
9.8%
517387
 
9.8%
I516252
 
9.8%
N372413
 
7.0%
O369603
 
7.0%
T321967
 
6.1%
R315619
 
6.0%
L309783
 
5.9%
A307956
 
5.8%
G265384
 
5.0%
Other values (23)1480122
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5294798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E518312
 
9.8%
517387
 
9.8%
I516252
 
9.8%
N372413
 
7.0%
O369603
 
7.0%
T321967
 
6.1%
R315619
 
6.0%
L309783
 
5.9%
A307956
 
5.8%
G265384
 
5.0%
Other values (23)1480122
28.0%

num_units
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0632997
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-10-08T23:11:21.319820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile3
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.39601188
Coefficient of variation (CV)0.19193135
Kurtosis35.766415
Mean2.0632997
Median Absolute Deviation (MAD)0
Skewness3.9882897
Sum431861
Variance0.15682541
MonotonicityNot monotonic
2025-10-08T23:11:21.466332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2189366
90.5%
312251
 
5.9%
15123
 
2.4%
41908
 
0.9%
5453
 
0.2%
6135
 
0.1%
734
 
< 0.1%
822
 
< 0.1%
99
 
< 0.1%
104
 
< 0.1%
ValueCountFrequency (%)
15123
 
2.4%
2189366
90.5%
312251
 
5.9%
41908
 
0.9%
5453
 
0.2%
6135
 
0.1%
734
 
< 0.1%
822
 
< 0.1%
99
 
< 0.1%
104
 
< 0.1%
ValueCountFrequency (%)
111
 
< 0.1%
104
 
< 0.1%
99
 
< 0.1%
822
 
< 0.1%
734
 
< 0.1%
6135
 
0.1%
5453
 
0.2%
41908
 
0.9%
312251
 
5.9%
2189366
90.5%

most_severe_injury
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
NO INDICATION OF INJURY
154789 
NONINCAPACITATING INJURY
31527 
REPORTED, NOT EVIDENT
16075 
INCAPACITATING INJURY
 
6564
FATAL
 
351

Length

Max length24
Median length23
Mean length22.904116
Min length5

Characters and Unicode

Total characters4793969
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO INDICATION OF INJURY
2nd rowNO INDICATION OF INJURY
3rd rowNO INDICATION OF INJURY
4th rowNONINCAPACITATING INJURY
5th rowNO INDICATION OF INJURY

Common Values

ValueCountFrequency (%)
NO INDICATION OF INJURY154789
74.0%
NONINCAPACITATING INJURY31527
 
15.1%
REPORTED, NOT EVIDENT16075
 
7.7%
INCAPACITATING INJURY6564
 
3.1%
FATAL351
 
0.2%

Length

2025-10-08T23:11:21.598996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T23:11:21.677138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
injury192880
25.9%
no154789
20.8%
indication154789
20.8%
of154789
20.8%
nonincapacitating31527
 
4.2%
reported16075
 
2.2%
not16075
 
2.2%
evident16075
 
2.2%
incapacitating6564
 
0.9%
fatal351
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N828633
17.3%
I787595
16.4%
534608
11.2%
O528044
11.0%
T279547
 
5.8%
A269764
 
5.6%
C230971
 
4.8%
R225030
 
4.7%
U192880
 
4.0%
Y192880
 
4.0%
Other values (9)724017
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4793969
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N828633
17.3%
I787595
16.4%
534608
11.2%
O528044
11.0%
T279547
 
5.8%
A269764
 
5.6%
C230971
 
4.8%
R225030
 
4.7%
U192880
 
4.0%
Y192880
 
4.0%
Other values (9)724017
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4793969
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N828633
17.3%
I787595
16.4%
534608
11.2%
O528044
11.0%
T279547
 
5.8%
A269764
 
5.6%
C230971
 
4.8%
R225030
 
4.7%
U192880
 
4.0%
Y192880
 
4.0%
Other values (9)724017
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4793969
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N828633
17.3%
I787595
16.4%
534608
11.2%
O528044
11.0%
T279547
 
5.8%
A269764
 
5.6%
C230971
 
4.8%
R225030
 
4.7%
U192880
 
4.0%
Y192880
 
4.0%
Other values (9)724017
15.1%

injuries_total
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38271717
Minimum0
Maximum21
Zeros154789
Zeros (%)74.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-10-08T23:11:21.770965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79971998
Coefficient of variation (CV)2.0895848
Kurtosis23.184727
Mean0.38271717
Median Absolute Deviation (MAD)0
Skewness3.4144456
Sum80105
Variance0.63955205
MonotonicityNot monotonic
2025-10-08T23:11:21.856966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0154789
74.0%
138378
 
18.3%
210447
 
5.0%
33505
 
1.7%
41338
 
0.6%
5488
 
0.2%
6212
 
0.1%
780
 
< 0.1%
830
 
< 0.1%
914
 
< 0.1%
Other values (9)25
 
< 0.1%
ValueCountFrequency (%)
0154789
74.0%
138378
 
18.3%
210447
 
5.0%
33505
 
1.7%
41338
 
0.6%
5488
 
0.2%
6212
 
0.1%
780
 
< 0.1%
830
 
< 0.1%
914
 
< 0.1%
ValueCountFrequency (%)
212
 
< 0.1%
191
 
< 0.1%
171
 
< 0.1%
161
 
< 0.1%
154
 
< 0.1%
131
 
< 0.1%
123
 
< 0.1%
115
 
< 0.1%
107
< 0.1%
914
< 0.1%

injuries_fatal
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0.0
208955 
1.0
 
317
2.0
 
30
3.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters627918
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0208955
99.8%
1.0317
 
0.2%
2.030
 
< 0.1%
3.04
 
< 0.1%

Length

2025-10-08T23:11:21.951168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T23:11:22.018649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0208955
99.8%
1.0317
 
0.2%
2.030
 
< 0.1%
3.04
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0418261
66.6%
.209306
33.3%
1317
 
0.1%
230
 
< 0.1%
34
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)627918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0418261
66.6%
.209306
33.3%
1317
 
0.1%
230
 
< 0.1%
34
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)627918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0418261
66.6%
.209306
33.3%
1317
 
0.1%
230
 
< 0.1%
34
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)627918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0418261
66.6%
.209306
33.3%
1317
 
0.1%
230
 
< 0.1%
34
 
< 0.1%

injuries_incapacitating
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.038102109
Minimum0
Maximum7
Zeros202672
Zeros (%)96.8%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-10-08T23:11:22.083081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.23396356
Coefficient of variation (CV)6.1404361
Kurtosis101.49999
Mean0.038102109
Median Absolute Deviation (MAD)0
Skewness8.4430419
Sum7975
Variance0.054738949
MonotonicityNot monotonic
2025-10-08T23:11:22.162188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0202672
96.8%
15682
 
2.7%
2683
 
0.3%
3182
 
0.1%
462
 
< 0.1%
519
 
< 0.1%
64
 
< 0.1%
72
 
< 0.1%
ValueCountFrequency (%)
0202672
96.8%
15682
 
2.7%
2683
 
0.3%
3182
 
0.1%
462
 
< 0.1%
519
 
< 0.1%
64
 
< 0.1%
72
 
< 0.1%
ValueCountFrequency (%)
72
 
< 0.1%
64
 
< 0.1%
519
 
< 0.1%
462
 
< 0.1%
3182
 
0.1%
2683
 
0.3%
15682
 
2.7%
0202672
96.8%

injuries_non_incapacitating
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22124067
Minimum0
Maximum21
Zeros176306
Zeros (%)84.2%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-10-08T23:11:22.248524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6149597
Coefficient of variation (CV)2.7795961
Kurtosis41.053424
Mean0.22124067
Median Absolute Deviation (MAD)0
Skewness4.5350042
Sum46307
Variance0.37817543
MonotonicityNot monotonic
2025-10-08T23:11:22.649944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0176306
84.2%
124413
 
11.7%
25688
 
2.7%
31828
 
0.9%
4667
 
0.3%
5232
 
0.1%
6106
 
0.1%
733
 
< 0.1%
815
 
< 0.1%
105
 
< 0.1%
Other values (9)13
 
< 0.1%
ValueCountFrequency (%)
0176306
84.2%
124413
 
11.7%
25688
 
2.7%
31828
 
0.9%
4667
 
0.3%
5232
 
0.1%
6106
 
0.1%
733
 
< 0.1%
815
 
< 0.1%
95
 
< 0.1%
ValueCountFrequency (%)
211
 
< 0.1%
191
 
< 0.1%
181
 
< 0.1%
161
 
< 0.1%
141
 
< 0.1%
131
 
< 0.1%
121
 
< 0.1%
111
 
< 0.1%
105
< 0.1%
95
< 0.1%

injuries_reported_not_evident
Real number (ℝ)

High correlation  Zeros 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12151587
Minimum0
Maximum15
Zeros190619
Zeros (%)91.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-10-08T23:11:22.730400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.45086501
Coefficient of variation (CV)3.7103386
Kurtosis46.98613
Mean0.12151587
Median Absolute Deviation (MAD)0
Skewness5.4461894
Sum25434
Variance0.20327926
MonotonicityNot monotonic
2025-10-08T23:11:22.816556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0190619
91.1%
114029
 
6.7%
23302
 
1.6%
3904
 
0.4%
4289
 
0.1%
5105
 
0.1%
629
 
< 0.1%
715
 
< 0.1%
87
 
< 0.1%
93
 
< 0.1%
Other values (3)4
 
< 0.1%
ValueCountFrequency (%)
0190619
91.1%
114029
 
6.7%
23302
 
1.6%
3904
 
0.4%
4289
 
0.1%
5105
 
0.1%
629
 
< 0.1%
715
 
< 0.1%
87
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
151
 
< 0.1%
111
 
< 0.1%
102
 
< 0.1%
93
 
< 0.1%
87
 
< 0.1%
715
 
< 0.1%
629
 
< 0.1%
5105
 
0.1%
4289
 
0.1%
3904
0.4%

injuries_no_indication
Real number (ℝ)

Zeros 

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2440016
Minimum0
Maximum49
Zeros6229
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-10-08T23:11:22.921894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum49
Range49
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.241175
Coefficient of variation (CV)0.55310791
Kurtosis65.915881
Mean2.2440016
Median Absolute Deviation (MAD)0
Skewness3.7427867
Sum469683
Variance1.5405155
MonotonicityNot monotonic
2025-10-08T23:11:23.041221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2109130
52.1%
136148
 
17.3%
334350
 
16.4%
413453
 
6.4%
06229
 
3.0%
55781
 
2.8%
62458
 
1.2%
7945
 
0.5%
8395
 
0.2%
9188
 
0.1%
Other values (29)229
 
0.1%
ValueCountFrequency (%)
06229
 
3.0%
136148
 
17.3%
2109130
52.1%
334350
 
16.4%
413453
 
6.4%
55781
 
2.8%
62458
 
1.2%
7945
 
0.5%
8395
 
0.2%
9188
 
0.1%
ValueCountFrequency (%)
491
< 0.1%
461
< 0.1%
422
< 0.1%
391
< 0.1%
371
< 0.1%
362
< 0.1%
351
< 0.1%
341
< 0.1%
321
< 0.1%
311
< 0.1%

crash_hour
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.373047
Minimum0
Maximum23
Zeros4487
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-10-08T23:11:23.145300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median14
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.6038304
Coefficient of variation (CV)0.41903916
Kurtosis-0.40990009
Mean13.373047
Median Absolute Deviation (MAD)4
Skewness-0.4354833
Sum2799059
Variance31.402915
MonotonicityNot monotonic
2025-10-08T23:11:23.242208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1716097
 
7.7%
1616025
 
7.7%
1515894
 
7.6%
1813431
 
6.4%
1413349
 
6.4%
1311949
 
5.7%
1211726
 
5.6%
811381
 
5.4%
1110052
 
4.8%
199683
 
4.6%
Other values (14)79719
38.1%
ValueCountFrequency (%)
04487
 
2.1%
13729
 
1.8%
23080
 
1.5%
32364
 
1.1%
42104
 
1.0%
52819
 
1.3%
64772
2.3%
79405
4.5%
811381
5.4%
99300
4.4%
ValueCountFrequency (%)
235943
 
2.8%
227035
3.4%
217472
3.6%
208219
3.9%
199683
4.6%
1813431
6.4%
1716097
7.7%
1616025
7.7%
1515894
7.6%
1413349
6.4%

crash_day_of_week
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1440236
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-10-08T23:11:23.324322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9668637
Coefficient of variation (CV)0.47462656
Kurtosis-1.2194932
Mean4.1440236
Median Absolute Deviation (MAD)2
Skewness-0.094234655
Sum867369
Variance3.8685526
MonotonicityNot monotonic
2025-10-08T23:11:23.403358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
634458
16.5%
530787
14.7%
730710
14.7%
430093
14.4%
330074
14.4%
227938
13.3%
125246
12.1%
ValueCountFrequency (%)
125246
12.1%
227938
13.3%
330074
14.4%
430093
14.4%
530787
14.7%
634458
16.5%
730710
14.7%
ValueCountFrequency (%)
730710
14.7%
634458
16.5%
530787
14.7%
430093
14.4%
330074
14.4%
227938
13.3%
125246
12.1%

crash_month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7718221
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 MiB
2025-10-08T23:11:23.492686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4275929
Coefficient of variation (CV)0.50615519
Kurtosis-1.1731058
Mean6.7718221
Median Absolute Deviation (MAD)3
Skewness-0.12253128
Sum1417383
Variance11.748393
MonotonicityNot monotonic
2025-10-08T23:11:23.586818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1020089
9.6%
919018
9.1%
1218816
9.0%
818350
8.8%
1118328
8.8%
617851
8.5%
717834
8.5%
517432
8.3%
116606
7.9%
315265
7.3%
Other values (2)29717
14.2%
ValueCountFrequency (%)
116606
7.9%
214621
7.0%
315265
7.3%
415096
7.2%
517432
8.3%
617851
8.5%
717834
8.5%
818350
8.8%
919018
9.1%
1020089
9.6%
ValueCountFrequency (%)
1218816
9.0%
1118328
8.8%
1020089
9.6%
919018
9.1%
818350
8.8%
717834
8.5%
617851
8.5%
517432
8.3%
415096
7.2%
315265
7.3%

injury_severity
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
154789 
2
31527 
1
16075 
3
 
6564
4
 
351

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters209306
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0154789
74.0%
231527
 
15.1%
116075
 
7.7%
36564
 
3.1%
4351
 
0.2%

Length

2025-10-08T23:11:23.676546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-08T23:11:23.743578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0154789
74.0%
231527
 
15.1%
116075
 
7.7%
36564
 
3.1%
4351
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0154789
74.0%
231527
 
15.1%
116075
 
7.7%
36564
 
3.1%
4351
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)209306
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0154789
74.0%
231527
 
15.1%
116075
 
7.7%
36564
 
3.1%
4351
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)209306
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0154789
74.0%
231527
 
15.1%
116075
 
7.7%
36564
 
3.1%
4351
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)209306
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0154789
74.0%
231527
 
15.1%
116075
 
7.7%
36564
 
3.1%
4351
 
0.2%

Interactions

2025-10-08T23:11:15.297251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:05.525738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:06.742768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:08.455023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:09.732617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:10.781870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:12.116407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:13.193023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:14.216451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:15.404590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:05.645935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:07.116285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:08.632337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:09.841941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:10.896336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:12.228644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:13.299832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:14.329607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:15.518627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:05.753338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:07.272487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:08.823236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:09.963709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:11.031989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:12.348287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:13.405737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:14.445584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:15.886781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:05.869274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:07.449492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:08.984644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:10.090404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:11.371898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:12.476300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:13.523100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:14.565630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:16.002642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:05.981828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:07.603169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:09.110080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:10.197162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:11.497037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:12.591295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:13.627848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:14.678406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:16.127813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:06.102544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:07.774512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:09.237705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:10.327811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:11.622442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:12.710627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:13.745636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:14.800912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:16.258531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:06.228702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:07.939437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:09.370358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:10.448540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:11.741179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:12.825918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:13.859953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:14.922514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:16.369752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:06.387687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:08.111301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:09.488108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:10.558266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:11.854684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:12.939069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:13.970380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:15.040803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:16.491429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:06.562912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:08.280513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:09.610947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:10.673890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:11.993282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:13.074438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:14.100581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-08T23:11:15.180332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-08T23:11:23.844903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
alignmentcrash_day_of_weekcrash_hourcrash_monthcrash_typedamagefirst_crash_typeinjuries_fatalinjuries_incapacitatinginjuries_no_indicationinjuries_non_incapacitatinginjuries_reported_not_evidentinjuries_totalinjury_severityintersection_related_ilighting_conditionmost_severe_injurynum_unitsprim_contributory_causeroad_defectroadway_surface_condtraffic_control_devicetrafficway_typeweather_condition
alignment1.0000.0020.0110.0020.0150.0100.0310.0000.0090.0000.0000.0000.0000.0070.0140.0080.0070.0000.0340.0180.0160.0150.0570.014
crash_day_of_week0.0021.0000.057-0.0060.0490.0280.0240.006-0.0060.012-0.014-0.006-0.0160.0140.0020.0630.0140.0040.0290.0030.0110.0110.0100.016
crash_hour0.0110.0571.0000.0040.1270.0580.0560.017-0.0040.058-0.007-0.004-0.0070.0360.0170.3930.0360.0200.0660.0270.0380.0280.0250.040
crash_month0.002-0.0060.0041.0000.0190.0080.0300.0020.004-0.0090.0120.0080.0150.0200.0060.0840.0200.0030.0330.0100.1390.0080.0120.102
crash_type0.0150.0490.1270.0191.0000.2660.3900.0460.2040.0010.1340.1700.1890.6700.0390.1210.6700.1650.3200.0700.1130.0830.1430.097
damage0.0100.0280.0580.0080.2661.0000.3200.0110.0450.0240.0450.0430.0610.1120.0260.0400.1120.0730.1230.0140.0220.0420.0780.015
first_crash_type0.0310.0240.0560.0300.3900.3201.0000.0320.0810.0230.0270.0250.0360.2180.1730.0500.2180.0430.2590.0750.0470.1120.0730.030
injuries_fatal0.0000.0060.0170.0020.0460.0110.0321.0000.0600.0000.0300.0110.1200.5770.0040.0140.5770.0440.0280.0010.0030.0010.0090.001
injuries_incapacitating0.009-0.006-0.0040.0040.2040.0450.0810.0601.000-0.1670.028-0.0090.3100.4980.0050.0140.4980.0460.0390.0090.0100.0090.0100.008
injuries_no_indication0.0000.0120.058-0.0090.0010.0240.0230.000-0.1671.000-0.370-0.231-0.4840.0270.0000.0110.0270.2210.0260.0090.0060.0050.0050.007
injuries_non_incapacitating0.000-0.014-0.0070.0120.1340.0450.0270.0300.028-0.3701.000-0.0320.7380.1320.0080.0130.1320.0900.0250.0000.0070.0060.0100.000
injuries_reported_not_evident0.000-0.006-0.0040.0080.1700.0430.0250.011-0.009-0.231-0.0321.0000.5360.2240.0100.0080.2240.0730.0180.0040.0070.0060.0110.005
injuries_total0.000-0.016-0.0070.0150.1890.0610.0360.1200.310-0.4840.7380.5361.0000.1530.0130.0200.1530.1180.0350.0060.0110.0060.0150.005
injury_severity0.0070.0140.0360.0200.6700.1120.2180.5770.4980.0270.1320.2240.1531.0000.0320.0351.0000.0680.1170.0200.0350.0280.0490.031
intersection_related_i0.0140.0020.0170.0060.0390.0260.1730.0040.0050.0000.0080.0100.0130.0321.0000.0190.0320.0190.0970.0140.0110.1900.1320.012
lighting_condition0.0080.0630.3930.0840.1210.0400.0500.0140.0140.0110.0130.0080.0200.0350.0191.0000.0350.0150.0830.1260.2250.1270.0750.298
most_severe_injury0.0070.0140.0360.0200.6700.1120.2180.5770.4980.0270.1320.2240.1531.0000.0320.0351.0000.0680.1170.0200.0350.0280.0490.031
num_units0.0000.0040.0200.0030.1650.0730.0430.0440.0460.2210.0900.0730.1180.0680.0190.0150.0681.0000.0440.0070.0130.0190.0070.009
prim_contributory_cause0.0340.0290.0660.0330.3200.1230.2590.0280.0390.0260.0250.0180.0350.1170.0970.0830.1170.0441.0000.1350.1540.1120.0560.099
road_defect0.0180.0030.0270.0100.0700.0140.0750.0010.0090.0090.0000.0040.0060.0200.0140.1260.0200.0070.1351.0000.2170.0950.0540.137
roadway_surface_cond0.0160.0110.0380.1390.1130.0220.0470.0030.0100.0060.0070.0070.0110.0350.0110.2250.0350.0130.1540.2171.0000.1000.0580.505
traffic_control_device0.0150.0110.0280.0080.0830.0420.1120.0010.0090.0050.0060.0060.0060.0280.1900.1270.0280.0190.1120.0950.1001.0000.0780.077
trafficway_type0.0570.0100.0250.0120.1430.0780.0730.0090.0100.0050.0100.0110.0150.0490.1320.0750.0490.0070.0560.0540.0580.0781.0000.045
weather_condition0.0140.0160.0400.1020.0970.0150.0300.0010.0080.0070.0000.0050.0050.0310.0120.2980.0310.0090.0990.1370.5050.0770.0451.000

Missing values

2025-10-08T23:11:16.814012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-08T23:11:17.462117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

crash_datetraffic_control_deviceweather_conditionlighting_conditionfirst_crash_typetrafficway_typealignmentroadway_surface_condroad_defectcrash_typeintersection_related_idamageprim_contributory_causenum_unitsmost_severe_injuryinjuries_totalinjuries_fatalinjuries_incapacitatinginjuries_non_incapacitatinginjuries_reported_not_evidentinjuries_no_indicationcrash_hourcrash_day_of_weekcrash_monthinjury_severity
007/29/2023 01:00:00 PMTRAFFIC SIGNALCLEARDAYLIGHTTURNINGNOT DIVIDEDSTRAIGHT AND LEVELUNKNOWNUNKNOWNNO INJURY / DRIVE AWAYY$501 - $1,500UNABLE TO DETERMINE2NO INDICATION OF INJURY0.00.00.00.00.03.013770
108/13/2023 12:11:00 AMTRAFFIC SIGNALCLEARDARKNESS, LIGHTED ROADTURNINGFOUR WAYSTRAIGHT AND LEVELDRYNO DEFECTSNO INJURY / DRIVE AWAYYOVER $1,500IMPROPER TURNING/NO SIGNAL2NO INDICATION OF INJURY0.00.00.00.00.02.00180
212/09/2021 10:30:00 AMTRAFFIC SIGNALCLEARDAYLIGHTREAR ENDT-INTERSECTIONSTRAIGHT AND LEVELDRYNO DEFECTSNO INJURY / DRIVE AWAYY$501 - $1,500FOLLOWING TOO CLOSELY3NO INDICATION OF INJURY0.00.00.00.00.03.0105120
308/09/2023 07:55:00 PMTRAFFIC SIGNALCLEARDAYLIGHTANGLEFOUR WAYSTRAIGHT AND LEVELDRYNO DEFECTSINJURY AND / OR TOW DUE TO CRASHYOVER $1,500UNABLE TO DETERMINE2NONINCAPACITATING INJURY5.00.00.05.00.00.019482
408/19/2023 02:55:00 PMTRAFFIC SIGNALCLEARDAYLIGHTREAR ENDT-INTERSECTIONSTRAIGHT AND LEVELUNKNOWNUNKNOWNNO INJURY / DRIVE AWAYY$501 - $1,500DRIVING SKILLS/KNOWLEDGE/EXPERIENCE2NO INDICATION OF INJURY0.00.00.00.00.03.014780
509/06/2023 12:59:00 AMNO CONTROLSRAINDARKNESS, LIGHTED ROADFIXED OBJECTNOT DIVIDEDSTRAIGHT AND LEVELWETUNKNOWNINJURY AND / OR TOW DUE TO CRASHN$501 - $1,500UNABLE TO DETERMINE1NONINCAPACITATING INJURY2.00.00.02.00.00.00492
612/20/2022 11:45:00 AMTRAFFIC SIGNALCLEARDAYLIGHTREAR TO FRONTFOUR WAYSTRAIGHT AND LEVELDRYNO DEFECTSNO INJURY / DRIVE AWAYY$501 - $1,500IMPROPER BACKING2NO INDICATION OF INJURY0.00.00.00.00.02.0113120
709/20/2023 02:38:00 PMNO CONTROLSCLEARDAYLIGHTANGLEDIVIDED - W/MEDIAN (NOT RAISED)CURVE, LEVELDRYNO DEFECTSINJURY AND / OR TOW DUE TO CRASHYOVER $1,500FAILING TO YIELD RIGHT-OF-WAY2NONINCAPACITATING INJURY1.00.00.01.00.01.014492
806/04/2018 06:42:00 PMTRAFFIC SIGNALCLEARDAYLIGHTREAR ENDNOT DIVIDEDSTRAIGHT AND LEVELDRYNO DEFECTSNO INJURY / DRIVE AWAYYOVER $1,500FOLLOWING TOO CLOSELY2NO INDICATION OF INJURY0.00.00.00.00.03.018260
909/07/2023 05:30:00 PMSTOP SIGN/FLASHERCLEARDAYLIGHTANGLEFOUR WAYSTRAIGHT AND LEVELDRYNO DEFECTSNO INJURY / DRIVE AWAYYOVER $1,500FAILING TO YIELD RIGHT-OF-WAY2NO INDICATION OF INJURY0.00.00.00.00.04.017590
crash_datetraffic_control_deviceweather_conditionlighting_conditionfirst_crash_typetrafficway_typealignmentroadway_surface_condroad_defectcrash_typeintersection_related_idamageprim_contributory_causenum_unitsmost_severe_injuryinjuries_totalinjuries_fatalinjuries_incapacitatinginjuries_non_incapacitatinginjuries_reported_not_evidentinjuries_no_indicationcrash_hourcrash_day_of_weekcrash_monthinjury_severity
20929601/16/2025 11:50:00 PMTRAFFIC SIGNALCLEARDARKNESS, LIGHTED ROADHEAD ONFOUR WAYSTRAIGHT AND LEVELDRYUNKNOWNINJURY AND / OR TOW DUE TO CRASHYOVER $1,500UNABLE TO DETERMINE2NO INDICATION OF INJURY0.00.00.00.00.03.023510
20929701/17/2025 09:10:00 PMTRAFFIC SIGNALCLEARDARKNESS, LIGHTED ROADTURNINGDIVIDED - W/MEDIAN (NOT RAISED)STRAIGHT AND LEVELDRYNO DEFECTSNO INJURY / DRIVE AWAYYOVER $1,500IMPROPER OVERTAKING/PASSING2NO INDICATION OF INJURY0.00.00.00.00.02.021610
20929801/17/2025 07:30:00 AMTRAFFIC SIGNALCLEARDAYLIGHTANGLEFOUR WAYSTRAIGHT AND LEVELDRYNO DEFECTSNO INJURY / DRIVE AWAYYOVER $1,500DISREGARDING TRAFFIC SIGNALS2NO INDICATION OF INJURY0.00.00.00.00.02.07610
20929906/09/2020 08:00:00 PMSTOP SIGN/FLASHERRAINDARKNESS, LIGHTED ROADANGLEFOUR WAYSTRAIGHT AND LEVELWETNO DEFECTSNO INJURY / DRIVE AWAYYOVER $1,500UNABLE TO DETERMINE2NO INDICATION OF INJURY0.00.00.00.00.02.020360
20930005/28/2023 01:20:00 AMTRAFFIC SIGNALUNKNOWNUNKNOWNREAR ENDFOUR WAYSTRAIGHT AND LEVELUNKNOWNUNKNOWNNO INJURY / DRIVE AWAYYOVER $1,500UNABLE TO DETERMINE2NO INDICATION OF INJURY0.00.00.00.00.03.01150
20930109/13/2023 01:08:00 PMUNKNOWNUNKNOWNUNKNOWNTURNINGFOUR WAYSTRAIGHT AND LEVELUNKNOWNUNKNOWNNO INJURY / DRIVE AWAYYOVER $1,500UNABLE TO DETERMINE2NO INDICATION OF INJURY0.00.00.00.00.02.013490
20930207/18/2023 02:10:00 PMUNKNOWNCLEARDAYLIGHTSIDESWIPE SAME DIRECTIONNOT DIVIDEDSTRAIGHT AND LEVELDRYNO DEFECTSNO INJURY / DRIVE AWAYYOVER $1,500IMPROPER OVERTAKING/PASSING2NO INDICATION OF INJURY0.00.00.00.00.02.014370
20930310/23/2019 01:32:00 PMTRAFFIC SIGNALCLEARDAYLIGHTPEDESTRIANDIVIDED - W/MEDIAN (NOT RAISED)STRAIGHT ON GRADEDRYNO DEFECTSINJURY AND / OR TOW DUE TO CRASHN$500 OR LESSRELATED TO BUS STOP2INCAPACITATING INJURY2.00.02.00.00.00.0134103
20930406/01/2020 03:23:00 PMNO CONTROLSCLEARDAYLIGHTPEDESTRIANT-INTERSECTIONSTRAIGHT AND LEVELDRYNO DEFECTSINJURY AND / OR TOW DUE TO CRASHY$500 OR LESSVISION OBSCURED (SIGNS, TREE LIMBS, BUILDINGS, ETC.)2NONINCAPACITATING INJURY1.00.00.01.00.01.015262
20930512/16/2022 12:10:00 PMTRAFFIC SIGNALCLEARDAYLIGHTTURNINGFOUR WAYSTRAIGHT AND LEVELWETNO DEFECTSNO INJURY / DRIVE AWAYYOVER $1,500FAILING TO YIELD RIGHT-OF-WAY2NO INDICATION OF INJURY0.00.00.00.00.02.0126120

Duplicate rows

Most frequently occurring

crash_datetraffic_control_deviceweather_conditionlighting_conditionfirst_crash_typetrafficway_typealignmentroadway_surface_condroad_defectcrash_typeintersection_related_idamageprim_contributory_causenum_unitsmost_severe_injuryinjuries_totalinjuries_fatalinjuries_incapacitatinginjuries_non_incapacitatinginjuries_reported_not_evidentinjuries_no_indicationcrash_hourcrash_day_of_weekcrash_monthinjury_severity# duplicates
001/08/2020 12:20:00 PMTRAFFIC SIGNALCLEARDAYLIGHTTURNINGDIVIDED - W/MEDIAN (NOT RAISED)STRAIGHT AND LEVELDRYNO DEFECTSINJURY AND / OR TOW DUE TO CRASHY$500 OR LESSFOLLOWING TOO CLOSELY2REPORTED, NOT EVIDENT1.00.00.00.01.01.0124112
102/08/2020 03:40:00 PMTRAFFIC SIGNALCLEARDAYLIGHTANGLENOT DIVIDEDSTRAIGHT AND LEVELDRYNO DEFECTSINJURY AND / OR TOW DUE TO CRASHYOVER $1,500UNABLE TO DETERMINE2REPORTED, NOT EVIDENT1.00.00.00.01.02.0157212
202/14/2020 03:48:00 PMSTOP SIGN/FLASHERCLEARDAYLIGHTPEDESTRIANNOT DIVIDEDSTRAIGHT AND LEVELSNOW OR SLUSHNO DEFECTSINJURY AND / OR TOW DUE TO CRASHYOVER $1,500DISREGARDING STOP SIGN2INCAPACITATING INJURY2.00.01.00.01.00.0156232
302/14/2022 11:00:00 AMTRAFFIC SIGNALCLEARDAYLIGHTREAR ENDNOT DIVIDEDSTRAIGHT AND LEVELDRYNO DEFECTSNO INJURY / DRIVE AWAYYOVER $1,500UNABLE TO DETERMINE2NO INDICATION OF INJURY0.00.00.00.00.02.0112202
402/25/2020 08:10:00 AMTRAFFIC SIGNALSNOWDAYLIGHTREAR ENDT-INTERSECTIONSTRAIGHT AND LEVELWETNO DEFECTSINJURY AND / OR TOW DUE TO CRASHY$500 OR LESSFOLLOWING TOO CLOSELY3NONINCAPACITATING INJURY2.00.00.02.00.03.083222
503/02/2022 04:30:00 PMTRAFFIC SIGNALCLEARDAYLIGHTREAR ENDDIVIDED - W/MEDIAN (NOT RAISED)STRAIGHT AND LEVELDRYNO DEFECTSNO INJURY / DRIVE AWAYYOVER $1,500FOLLOWING TOO CLOSELY2NO INDICATION OF INJURY0.00.00.00.00.02.0164302
604/02/2018 01:00:00 PMTRAFFIC SIGNALCLEARDAYLIGHTREAR ENDNOT DIVIDEDSTRAIGHT AND LEVELDRYNO DEFECTSNO INJURY / DRIVE AWAYY$501 - $1,500FOLLOWING TOO CLOSELY2NO INDICATION OF INJURY0.00.00.00.00.02.0132402
705/05/2023 04:12:00 PMTRAFFIC SIGNALCLEARDAYLIGHTREAR ENDFOUR WAYSTRAIGHT AND LEVELDRYUNKNOWNNO INJURY / DRIVE AWAYY$501 - $1,500FOLLOWING TOO CLOSELY2NO INDICATION OF INJURY0.00.00.00.00.04.0166502
805/19/2020 11:42:00 PMTRAFFIC SIGNALCLEARDARKNESS, LIGHTED ROADANGLENOT DIVIDEDSTRAIGHT AND LEVELDRYNO DEFECTSINJURY AND / OR TOW DUE TO CRASHYOVER $1,500DISREGARDING TRAFFIC SIGNALS2NONINCAPACITATING INJURY3.00.00.03.00.01.0233522
905/29/2020 06:00:00 PMOTHERCLEARDAYLIGHTPEDALCYCLISTDIVIDED - W/MEDIAN BARRIERCURVE, LEVELDRYUNKNOWNINJURY AND / OR TOW DUE TO CRASHYOVER $1,500DISREGARDING ROAD MARKINGS2NONINCAPACITATING INJURY1.00.00.01.00.01.0186522